diff --git a/.gitignore b/.gitignore
index 811c0a9..cc831d7 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,5 +1,12 @@
# Data and configs
-*.toml
+#*.toml
+examples/slac_fel/data/
+# DS_Store files
+.DS_Store
+.idea
+
+double_check_data.ipynb
+uv.lock
# Logging files
*.db
diff --git a/examples/slac_fel/__init__.py b/examples/slac_fel/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/examples/slac_fel/model.py b/examples/slac_fel/model.py
new file mode 100644
index 0000000..2400030
--- /dev/null
+++ b/examples/slac_fel/model.py
@@ -0,0 +1,462 @@
+# examples/slac_fel/model.py
+"""SLAC FEL model harness for the BaseSim continuous-learning framework.
+
+This harness wraps a 7-layer ELU regression network with dropout regularisation,
+trained to predict HXR pulse intensity from accelerator settings. The pre-processed
+accelerator data is chronologically sorted and sliced into time windows,
+each of which is served in order by ``update_data_stream()``.
+"""
+
+from __future__ import annotations
+
+import gc
+import logging
+import os
+from typing import Any, List, Optional, Tuple
+
+import torch
+import torch.nn.functional as F
+from torch import Tensor, nn
+from torch.optim import Optimizer
+from torch.utils.data import ConcatDataset, DataLoader
+
+from apeiron.config.configuration import Config
+from apeiron.model.torch_model_harness import BaseModelHarness
+
+from examples.slac_fel.utils import (
+ FELDataset,
+ discover_window_files,
+ load_feature_config,
+ load_fel_data,
+ load_scalers,
+ load_window_file,
+ make_loader,
+ split_into_windows,
+ split_timestamps,
+)
+
+
+_log = logging.getLogger(__name__)
+
+
+# -------------------------------------------------------------------------------------------------
+# Neural-network architecture
+# Matches FELNeuralNetwork in train_fel_model.py from surrogate repo model arch on 4/20/2026
+# -------------------------------------------------------------------------------------------------
+class FELNet(nn.Module):
+ """7-layer fully-connected ELU regression network.
+ Predicts FEL pulse intensity from scaled accelerator inputs."""
+
+ def __init__(self, input_size=None, output_size=1):
+ super(FELNet, self).__init__()
+
+ self.net = nn.Sequential(
+ nn.Linear(input_size, 1024),
+ nn.ELU(),
+ nn.Linear(1024, 512),
+ nn.ELU(),
+ nn.Linear(512, 256),
+ nn.ELU(),
+ nn.Linear(256, 128),
+ nn.ELU(),
+ nn.Dropout(p=0.05),
+ nn.Linear(128, 64),
+ nn.ELU(),
+ nn.Dropout(p=0.05),
+ nn.Linear(64, 32),
+ nn.ELU(),
+ nn.Linear(32, 16),
+ nn.ELU(),
+ nn.Dropout(p=0.05),
+ nn.Linear(16, output_size),
+ nn.Softplus(beta=1.0, threshold=20.0),
+ )
+
+ def forward(self, x: Tensor) -> Tensor:
+ if not torch.isfinite(x).all():
+ n_bad = (~torch.isfinite(x)).sum().item()
+ _log.warning(
+ "FELNet.forward: %d non-finite values in input. Replacing with 0",
+ n_bad,
+ )
+ x = torch.nan_to_num(x, nan=0.0, posinf=1e6, neginf=-1e6)
+
+ out = self.net(x)
+
+ # Detect NaN outputs
+ if not torch.isfinite(out).all():
+ if torch.isnan(out).any():
+ _log.warning("Model produced NaN predictions. Replacing with 0.")
+ out = torch.nan_to_num(out, nan=0.0, posinf=1e6, neginf=-1e6)
+
+ return out
+
+
+# ---------------------------------------------------------------------------
+# Regression metrics (match MetricFn = Callable[[Tensor, Tensor], Any])
+# ---------------------------------------------------------------------------
+@torch.no_grad()
+def mse_metric(y_hat: Tensor, y: Tensor) -> Tensor:
+ """Mean-squared error computed on the batch."""
+ return F.mse_loss(y_hat, y)
+
+
+# ---------------------------------------------------------------------------
+# Model harness
+# ---------------------------------------------------------------------------
+
+# Fraction of each time window reserved for validation
+_VAL_FRACTION: float = 0.2
+
+
+class SLAC_FEL(BaseModelHarness):
+ """Continuous-learning harness for the LCLS FEL regression model.
+
+ The data path (``cfg.data.path``) must point to a directory containing:
+
+ * ``hxr_*.pkl`` – pre-filtered, chronologically-sorted DataFrames. Files
+ are loaded lazily one at a time as windows are consumed, keeping only
+ one file's worth of tensors in memory at once.
+ * **OR** ``data.pkl`` – a single monolithic DataFrame that will be split
+ into fixed-size windows via ``cfg.data.batch_size`` (legacy fallback).
+
+ The data path (``cfg.model``) must point to a directory containing:
+ * ``input_scaler.pt`` – BoTorch ``AffineInputTransform`` for inputs
+ * ``output_scaler.pt`` – BoTorch ``AffineInputTransform`` for the target
+ * ``feature_config.yml`` – YAML listing input / output variable names
+ * ``model_pretrained.pt`` (optional) – pretrained checkpoint for the FELNet model
+
+ Each call to :meth:`update_data_stream` advances to the next time window.
+ """
+
+ def __init__(self, cfg: Config) -> None:
+ # ----- scalers & feature config (always needed) ----------------------
+ assert cfg.model.config_path is not None, (
+ "model.config_path must be set for SLAC-FEL harness"
+ )
+ self.input_scaler, self.output_scaler = load_scalers(
+ cfg.model.config_path, device=cfg.device
+ )
+ self.input_cols, self.output_cols = load_feature_config(cfg.model.config_path)
+
+ # ----- discover per-file windows or fall back to monolithic ----------
+ self._window_file_paths = discover_window_files(cfg.data.path)
+ self._lazy = len(self._window_file_paths) > 0
+
+ # Dimensions come from the feature config – no data loading needed.
+ input_size = len(self.input_cols)
+ output_size = len(self.output_cols)
+
+ if self._lazy:
+ # Lazy mode: files are loaded one at a time as windows are consumed.
+ self._file_idx: int = 0
+ self._active_windows: List[Tuple[Tensor, Tensor]] = []
+ self._active_timestamps: List = []
+ self._active_window_idx: int = 0
+ # num_windows is unknown until all files are scanned; use -1 as sentinel.
+ self.num_windows: int = -1
+ print(
+ f"[SLAC-FEL] Lazy loading: {len(self._window_file_paths)} file(s) in "
+ f"{cfg.data.path} (window_size={cfg.data.batch_size}, "
+ f"input_dim={input_size}, output_dim={output_size})"
+ )
+ else:
+ # Legacy mode: single data.pkl split into fixed-size windows.
+ X, y, timestamps = load_fel_data(cfg.data.path, cfg.model.config_path, device=cfg.device)
+ print(f"[SLAC-FEL] Legacy mode: single data file {cfg.data.path} with {X.shape[0]} samples")
+ self.windows = split_into_windows(X, y, window_size=cfg.data.batch_size)
+ self.window_timestamps = split_timestamps(
+ timestamps, window_size=cfg.data.batch_size
+ )
+ self.num_windows = len(self.windows)
+ print(
+ f"[SLAC-FEL] Legacy mode: {self.num_windows} windows "
+ f"(window_size={cfg.data.batch_size}, "
+ f"input_dim={input_size}, output_dim={output_size})"
+ )
+
+ # ----- build model ---------------------------------------------------
+ pretrained_path = cfg.model.pretrained_path
+ if pretrained_path:
+ model = self._load_pretrained_direct(
+ pretrained_path, input_size, output_size, cfg.device
+ )
+ else:
+ model = FELNet(input_size=input_size, output_size=output_size)
+
+ super().__init__(cfg=cfg, model=model)
+
+ # ----- eval metrics (regression) -------------------------------------
+ self.eval_metrics = {"mse": mse_metric}
+ self.higher_is_better = {"mse": False}
+
+ # ----- streaming state -----------------------------------------------
+ self.window_idx: int = 0
+ self.history_windows: List[Tuple[Tensor, Tensor]] = []
+ self._current_window: Optional[Tuple[Tensor, Tensor]] = None
+ self.current_window_timerange: Optional[Tuple[str, str]] = None
+
+ # Cap history to prevent unbounded memory growth
+ self.max_history_windows: int = 20
+
+ self._cur_train_loader: Optional[DataLoader] = None
+ self._cur_val_loader: Optional[DataLoader] = None
+
+ # --------------------------------------------------------------------- #
+ # Required overrides
+ # --------------------------------------------------------------------- #
+
+ def get_optmizer(self) -> Optimizer: # noqa: D102 (spelling kept for ABC)
+ return torch.optim.Adam(self.model.parameters(), lr=self.cfg.train.init_lr)
+
+ def get_criterion(self): # noqa: D102
+ return nn.MSELoss()
+
+ def get_stream_dataloader(self):
+ assert self._cur_val_loader is not None
+ return self._cur_val_loader
+
+ def get_train_dataloaders(self):
+ assert self._cur_train_loader is not None and self._cur_val_loader is not None
+ return self._cur_train_loader, self._cur_val_loader
+
+ def get_hist_dataloaders(
+ self,
+ ) -> Tuple[Optional[DataLoader], Optional[DataLoader]]:
+ """Return loaders over all previously-seen time windows.
+
+ Returns ``(None, None)`` until at least two windows have been served.
+ """
+ if self.window_idx <= 1:
+ return None, None
+
+ # Concatenate all history windows
+ hist_train_views: List[FELDataset] = []
+ hist_val_views: List[FELDataset] = []
+
+ for X_w, y_w in self.history_windows:
+ n = X_w.shape[0]
+ n_val = max(1, int(n * _VAL_FRACTION))
+ n_train = n - n_val
+ hist_train_views.append(FELDataset(X_w[:n_train], y_w[:n_train]))
+ hist_val_views.append(FELDataset(X_w[n_train:], y_w[n_train:]))
+
+ ds_hist_train: ConcatDataset[Any] = ConcatDataset(hist_train_views)
+ ds_hist_val: ConcatDataset[Any] = ConcatDataset(hist_val_views)
+
+ bs = self.cfg.train.batch_size
+ nw = self.cfg.train.num_workers
+ pin = torch.cuda.is_available()
+
+ return (
+ make_loader(
+ ds_hist_train, bs, shuffle=True, num_workers=nw, pin_memory=pin
+ ),
+ make_loader(ds_hist_val, bs, shuffle=False, num_workers=nw, pin_memory=pin),
+ )
+
+ def _load_active_file(self) -> None:
+ """Load the next pkl file into ``_active_windows`` / ``_active_timestamps``.
+
+ Wraps around to the first file once all files have been consumed and
+ frees the previous file's tensors (those no longer referenced by
+ ``history_windows``) via an explicit GC pass.
+ """
+ if self._file_idx >= len(self._window_file_paths):
+ print(
+ f"[SLAC-FEL] All {len(self._window_file_paths)} file(s) exhausted; "
+ "wrapping around to the first file."
+ )
+ self._file_idx = 0
+
+ pkl_path = self._window_file_paths[self._file_idx]
+ print(
+ f"[SLAC-FEL] Loading file "
+ f"{self._file_idx + 1}/{len(self._window_file_paths)}: "
+ f"{os.path.basename(pkl_path)}"
+ )
+
+ X_w, y_w, idx = load_window_file(
+ pkl_path,
+ self.input_cols,
+ self.output_cols,
+ self.input_scaler,
+ self.output_scaler,
+ )
+ self._active_windows = split_into_windows(
+ X_w, y_w, window_size=self.cfg.data.batch_size
+ )
+ self._active_timestamps = split_timestamps(idx, window_size=self.cfg.data.batch_size)
+ self._active_window_idx = 0
+ self._file_idx += 1
+ gc.collect()
+ print(
+ f"[SLAC-FEL] Ready: {len(self._active_windows)} windows from "
+ f"{os.path.basename(pkl_path)} "
+ f"({X_w.shape[0]} samples, window_size={self.cfg.data.batch_size})"
+ )
+
+ def update_data_stream(self) -> None:
+ """Advance to the next chronological time window.
+
+ In lazy mode each pkl file is loaded on demand when the current file's
+ windows are exhausted. In legacy mode all windows are already in memory.
+ """
+ self._dispose_current_loaders()
+
+ # ── Archive the *previous* window into history ────────────────────
+ if self._current_window is not None:
+ self.history_windows.append(self._current_window)
+ self._current_window = None
+ # Evict oldest windows when cap is reached
+ while len(self.history_windows) > self.max_history_windows:
+ self.history_windows.pop(0)
+
+ # ── Fetch the next window tensors ─────────────────────────────────
+ if self._lazy:
+ # Load a new file if we've consumed all windows from the current one.
+ if self._active_window_idx >= len(self._active_windows):
+ self._load_active_file()
+
+ X_w, y_w = self._active_windows[self._active_window_idx]
+ ts = self._active_timestamps[self._active_window_idx]
+ self._active_window_idx += 1
+ window_label = (
+ f"{self._file_idx}/{len(self._window_file_paths)} "
+ f"(win {self._active_window_idx}/{len(self._active_windows)} in file)"
+ )
+ else:
+ if self.window_idx >= self.num_windows:
+ print(
+ f"Warning: All {self.num_windows} time windows exhausted; "
+ "wrapping around to the first window."
+ )
+ self.window_idx = 0
+
+ X_w, y_w = self.windows[self.window_idx]
+ ts = self.window_timestamps[self.window_idx]
+ window_label = f"{self.window_idx + 1}/{self.num_windows}"
+
+ # Record timestamp range for this window
+ self.current_window_timerange = (str(ts[0]), str(ts[-1]))
+
+ # Keep a reference so the next call can archive it without reloading
+ self._current_window = (X_w, y_w)
+
+ # Train / val split (last _VAL_FRACTION chronologically)
+ n = X_w.shape[0]
+ n_val = max(1, int(n * _VAL_FRACTION))
+ n_train = n - n_val
+ # Safety: ensure both splits have at least 1 sample
+ if n_train < 1:
+ n_train = max(1, n - 1)
+ n_val = n - n_train
+
+ ds_train = FELDataset(X_w[:n_train], y_w[:n_train])
+ ds_val = FELDataset(X_w[n_train:], y_w[n_train:])
+
+ bs = self.cfg.train.batch_size
+ nw = self.cfg.train.num_workers
+ pin = torch.cuda.is_available()
+
+ self._cur_train_loader = make_loader(
+ ds_train, bs, shuffle=True, num_workers=nw, pin_memory=pin
+ )
+ self._cur_val_loader = make_loader(
+ ds_val, bs, shuffle=False, num_workers=nw, pin_memory=pin
+ )
+
+ print(
+ f"[SLAC-FEL] Window {window_label}: "
+ f"{n_train} train / {n_val} val samples "
+ f"[{self.current_window_timerange[0]} → {self.current_window_timerange[1]}]"
+ )
+ self.window_idx += 1
+
+ # --------------------------------------------------------------------- #
+ # Helpers
+ # --------------------------------------------------------------------- #
+
+ @staticmethod
+ def _load_pretrained_direct(
+ path: str, input_size: int, output_size: int, device: str
+ ) -> FELNet:
+ """Load a pretrained checkpoint directly with no weight modifications.
+
+ Supports two save formats produced by ``train_fel_model.py``:
+
+ 1. A raw ``nn.Sequential`` (saved via ``torch.save(model.net, ...)``).
+ The Sequential is wrapped inside a new :class:`FELNet` whose
+ architecture is defined entirely by the checkpoint.
+ 2. A ``state_dict`` (plain ``dict``). The input dimension is inferred
+ from the first Linear layer's weight shape so that :class:`FELNet`
+ is constructed to match exactly, then ``load_state_dict`` is called
+ with ``strict=True``.
+
+ Raises:
+ FileNotFoundError: If *path* does not exist.
+ RuntimeError: If the checkpoint shapes are incompatible with the
+ data (e.g. the data has a different number of input features
+ than the model expects).
+ """
+ state = torch.load(path, map_location=device, weights_only=False)
+
+ if isinstance(state, nn.Sequential):
+ # Format 1: checkpoint is the raw nn.Sequential
+ # Infer input/output dims from the first and last Linear layers
+ first_linear = next(m for m in state.modules() if isinstance(m, nn.Linear))
+ last_linear = list(m for m in state.modules() if isinstance(m, nn.Linear))[
+ -1
+ ]
+ ckpt_in = first_linear.in_features
+ ckpt_out = last_linear.out_features
+
+ if ckpt_in != input_size:
+ raise RuntimeError(
+ f"Pretrained model expects {ckpt_in} input features but "
+ f"the data has {input_size}. Ensure the feature_config.yml "
+ f"and scalers match the checkpoint."
+ )
+
+ model = FELNet(input_size=ckpt_in, output_size=ckpt_out)
+ model.net.load_state_dict(state.state_dict(), strict=True)
+ print(f"Loaded pretrained FEL model (nn.Sequential) from {path}")
+
+ elif isinstance(state, dict):
+ # Format 2: checkpoint is a state_dict
+ # Strip torch.compile artefact from keys
+ sd = {k.replace("_orig_mod.", ""): v for k, v in state.items()}
+
+ # Infer input dim from the first weight tensor
+ first_weight_key = next(
+ k for k in sd if k.endswith(".weight") and sd[k].dim() == 2
+ )
+ ckpt_in = sd[first_weight_key].shape[1]
+
+ if ckpt_in != input_size:
+ raise RuntimeError(
+ f"Pretrained model expects {ckpt_in} input features but "
+ f"the data has {input_size}. Ensure the feature_config.yml "
+ f"and scalers match the checkpoint."
+ )
+
+ model = FELNet(input_size=ckpt_in, output_size=output_size)
+ model.load_state_dict(sd, strict=True)
+ print(f"Loaded pretrained FEL model (state_dict) from {path}")
+
+ else:
+ raise TypeError(
+ f"Unexpected checkpoint type {type(state).__name__} from {path}. "
+ f"Expected nn.Sequential or state_dict."
+ )
+
+ return model
+
+ def _dispose_current_loaders(self) -> None:
+ if self._cur_train_loader is not None:
+ del self._cur_train_loader
+ self._cur_train_loader = None
+ if self._cur_val_loader is not None:
+ del self._cur_val_loader
+ self._cur_val_loader = None
+ gc.collect()
diff --git a/examples/slac_fel/model/feature_config.yml b/examples/slac_fel/model/feature_config.yml
new file mode 100644
index 0000000..c62e96d
--- /dev/null
+++ b/examples/slac_fel/model/feature_config.yml
@@ -0,0 +1,2743 @@
+device: cpu
+input_transformers:
+- resources/lcls_fel_input_scaler.pt
+input_variables:
+ QUAD:LI21:211:BACT:
+ variable_class: TorchScalarVariable
+ default_value: 6.030075550079346
+ read_only: false
+ value_range:
+ - 3.2707419395446777
+ - 7.083395004272461
+ unit: kG
+ QUAD:LI21:221:BACT:
+ variable_class: TorchScalarVariable
+ default_value: -0.22167986631393433
+ read_only: false
+ value_range:
+ - -0.7424343228340149
+ - 0.2432546466588974
+ unit: kG
+ QUAD:LI21:243:BACT:
+ variable_class: TorchScalarVariable
+ default_value: -0.0006752711487933993
+ read_only: false
+ value_range:
+ - -0.0007149674347601831
+ - -0.0006156182498671114
+ unit: kG-m
+ QUAD:LI21:251:BACT:
+ variable_class: TorchScalarVariable
+ default_value: -0.14767540991306305
+ read_only: false
+ value_range:
+ - -0.7296757698059082
+ - 0.25308719277381897
+ unit: kG
+ QUAD:LI21:271:BACT:
+ variable_class: TorchScalarVariable
+ default_value: -6.2067694664001465
+ read_only: false
+ value_range:
+ - -7.801762104034424
+ - -4.287487983703613
+ unit: kG
+ QUAD:LI21:335:BACT:
+ variable_class: TorchScalarVariable
+ default_value: -5.328253269195557
+ read_only: false
+ value_range:
+ - -5.428651332855225
+ - -5.321958065032959
+ unit: kG
+ QUAD:LI24:713:BACT:
+ variable_class: TorchScalarVariable
+ default_value: 35.41676330566406
+ read_only: false
+ value_range:
+ - 23.610397338867188
+ - 35.42658615112305
+ unit: kG
+ QUAD:LI24:740:BACT:
+ variable_class: TorchScalarVariable
+ default_value: -0.0005302699573803693
+ read_only: false
+ value_range:
+ - -0.9996497631072998
+ - 1.100592851638794
+ unit: kG
+ QUAD:LI24:860:BACT:
+ variable_class: TorchScalarVariable
+ default_value: 0.4042496532201767
+ read_only: false
+ value_range:
+ - -1.2825469970703125
+ - 2.1002092361450195
+ unit: kG
+ QUAD:LI24:892:BACT:
+ variable_class: TorchScalarVariable
+ default_value: -40.7828369140625
+ read_only: false
+ value_range:
+ - -40.78890609741211
+ - -27.187170028686523
+ unit: kG
+ QUAD:LI24:902:BACT:
+ variable_class: TorchScalarVariable
+ default_value: 17.336612701416016
+ read_only: false
+ value_range:
+ - 11.556938171386719
+ - 17.337291717529297
+ unit: kG
+ QUAD:CLTH:140:BACT:
+ variable_class: TorchScalarVariable
+ default_value: -21.54228973388672
+ read_only: false
+ value_range:
+ - -21.91339683532715
+ - -21.316192626953125
+ unit: kG
+ QUAD:CLTH:170:BACT:
+ variable_class: TorchScalarVariable
+ default_value: 11.69263744354248
+ read_only: false
+ value_range:
+ - 11.570372581481934
+ - 11.913098335266113
+ unit: kG
+ QUAD:BSYH:445:BACT:
+ variable_class: TorchScalarVariable
+ default_value: 39.05901336669922
+ read_only: false
+ value_range:
+ - 38.6727409362793
+ - 39.68069076538086
+ unit: kG
+ QUAD:LTUH:285:BACT:
+ variable_class: TorchScalarVariable
+ default_value: -68.72994232177734
+ read_only: false
+ value_range:
+ - -69.82388305664062
+ - -58.627498626708984
+ unit: kG
+ QUAD:LTUH:295:BACT:
+ variable_class: TorchScalarVariable
+ default_value: -68.79946899414062
+ read_only: false
+ value_range:
+ - -69.89398956298828
+ - -62.51885986328125
+ unit: kG
+ QUAD:LTUH:385:BACT:
+ variable_class: TorchScalarVariable
+ default_value: -68.75484466552734
+ read_only: false
+ value_range:
+ - -69.84967803955078
+ - -58.85036849975586
+ unit: kG
+ QUAD:LTUH:395:BACT:
+ variable_class: TorchScalarVariable
+ default_value: -68.72314453125
+ read_only: false
+ value_range:
+ - -69.81681823730469
+ - -58.62387466430664
+ unit: kG
+ QUAD:LTUH:440:BACT:
+ variable_class: TorchScalarVariable
+ default_value: -2.281162738800049
+ read_only: false
+ value_range:
+ - -5.002049922943115
+ - 4.872499942779541
+ unit: kG
+ QUAD:LTUH:460:BACT:
+ variable_class: TorchScalarVariable
+ default_value: -0.8954360485076904
+ read_only: false
+ value_range:
+ - -4.96889591217041
+ - 4.391031742095947
+ unit: kG
+ QUAD:LTUH:485:BACT:
+ variable_class: TorchScalarVariable
+ default_value: -68.74945831298828
+ read_only: false
+ value_range:
+ - -69.84385681152344
+ - -58.85063934326172
+ unit: kG
+ QUAD:LTUH:495:BACT:
+ variable_class: TorchScalarVariable
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+model: resources/lcls_fel_final_model_cpu.pt
+model_class: TorchModule
+output_format: tensor
+output_transformers:
+- resources/lcls_fel_output_scaler.pt
+output_variables:
+ GDET:FEE1:241:ENRC:
+ read_only: false
+ variable_class: TorchScalarVariable
+ unit: mJ
+precision: double
diff --git a/examples/slac_fel/model/final_lcls_fel_model.pt b/examples/slac_fel/model/final_lcls_fel_model.pt
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diff --git a/examples/slac_fel/model/final_lcls_fel_model_gpu.pt b/examples/slac_fel/model/final_lcls_fel_model_gpu.pt
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diff --git a/examples/slac_fel/model/lcls_fel_input_scaler.pt b/examples/slac_fel/model/lcls_fel_input_scaler.pt
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diff --git a/examples/slac_fel/model/lcls_fel_output_scaler.pt b/examples/slac_fel/model/lcls_fel_output_scaler.pt
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diff --git a/examples/slac_fel/plot.py b/examples/slac_fel/plot.py
new file mode 100644
index 0000000..9111e73
--- /dev/null
+++ b/examples/slac_fel/plot.py
@@ -0,0 +1,163 @@
+from pathlib import Path
+import gc
+import glob
+import re
+
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.dates as mdates
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import torch
+import yaml
+
+
+DATA_DIR = Path("data")
+MODEL_DIR = Path("model")
+OUTPUT_PATH = Path("march_2026_3_drifts_base_full.png")
+MAX_FILES = 230 # not using (plotting all)
+PREDICTION_BATCH_SIZE = 8192
+WINDOW_SIZE = 50
+GROUND_TRUTH_COLUMN = "GDET:FEE1:241:ENRC"
+
+DRIFTS = [
+ ("2026-03-15 10:46:05", "2026-03-15 12:07:17"),
+ ("2026-03-21 08:22:42", "2026-03-21 09:45:13"),
+ ("2026-03-26 16:54:16", "2026-03-26 18:42:51"),
+]
+
+UPDATED_CHECKPOINTS = [
+ (Path("../../output/slac-fel/drift_adaptation_1.pt"), "Updated Chkpt 1", DRIFTS[0][0]),
+ (Path("../../output/slac-fel/drift_adaptation_2.pt"), "Updated Chkpt 2", DRIFTS[1][0]),
+ (Path("../../output/slac-fel/drift_adaptation_3.pt"), "Updated Chkpt 3", DRIFTS[2][0]),
+]
+
+UPDATED_PLOT_RANGES = [
+ (DRIFTS[0][0], DRIFTS[1][0]),
+ (DRIFTS[1][0], DRIFTS[2][0]),
+ (DRIFTS[2][0], None),
+]
+
+
+def sorted_data_files(limit: int) -> list[str]:
+ files = glob.glob(str(DATA_DIR / "*.pkl"))
+ files.sort(key=lambda file_name: int(re.search(r"\d+", file_name).group()))
+ return files#[:limit]
+
+
+def load_feature_columns() -> tuple[list[str], list[str]]:
+ with (MODEL_DIR / "feature_config.yml").open("r", encoding="utf-8") as file_handle:
+ config = yaml.safe_load(file_handle)
+ input_columns = list(config["input_variables"].keys())
+ output_columns = list(config["output_variables"].keys())
+ return input_columns, output_columns
+
+
+def load_selected_frame(files: list[str], columns: list[str]) -> pd.DataFrame:
+ selected_columns = list(dict.fromkeys(columns))
+ frames = []
+ for file_name in files:
+ frame = pd.read_pickle(file_name)
+ frames.append(frame.loc[:, selected_columns])
+ return pd.concat(frames, copy=False)
+
+
+def predict_in_batches(model: torch.nn.Module, scaled_inputs: torch.Tensor, output_scaler) -> np.ndarray:
+ batches = []
+ model.eval()
+ with torch.inference_mode():
+ for start_index in range(0, scaled_inputs.shape[0], PREDICTION_BATCH_SIZE):
+ batch_inputs = scaled_inputs[start_index:start_index + PREDICTION_BATCH_SIZE]
+ batch_output = model(batch_inputs)
+ batch_unscaled = output_scaler.untransform(batch_output)
+ batches.append(batch_unscaled.detach().cpu().numpy().reshape(-1))
+ return np.concatenate(batches, axis=0)
+
+
+def load_checkpoint(model: torch.nn.Module, checkpoint_path: Path) -> None:
+ state_dict = torch.load(checkpoint_path, weights_only=True, map_location="cpu")
+ cleaned_state_dict = {key.replace("net.", ""): value for key, value in state_dict.items()}
+ model.load_state_dict(cleaned_state_dict)
+
+
+def align_timestamp(timestamp_text: str, index: pd.Index) -> pd.Timestamp:
+ timestamp = pd.Timestamp(timestamp_text)
+ index_timezone = getattr(index, "tz", None)
+ if index_timezone is not None and timestamp.tzinfo is None:
+ return timestamp.tz_localize(index_timezone)
+ if index_timezone is None and timestamp.tzinfo is not None:
+ return timestamp.tz_localize(None)
+ return timestamp
+
+
+def main() -> None:
+ input_columns, output_columns = load_feature_columns()
+ files = sorted_data_files(MAX_FILES)
+ selected_frame = load_selected_frame(files, input_columns + output_columns + [GROUND_TRUTH_COLUMN])
+
+ model = torch.load(MODEL_DIR / "final_lcls_fel_model.pt", weights_only=False, map_location="cpu")
+ input_scaler = torch.load(MODEL_DIR / "lcls_fel_input_scaler.pt", weights_only=False, map_location="cpu")
+ output_scaler = torch.load(MODEL_DIR / "lcls_fel_output_scaler.pt", weights_only=False, map_location="cpu")
+
+ raw_inputs = torch.as_tensor(selected_frame[input_columns].to_numpy(dtype=np.float32, copy=False))
+ scaled_inputs = input_scaler.transform(raw_inputs)
+
+ pretrained_prediction = predict_in_batches(model, scaled_inputs, output_scaler)
+
+ date_format = mdates.DateFormatter("%m-%d %H:%M")
+ fontsize = 12
+ fig, ax = plt.subplots(figsize=(12, 7))
+
+ ground_truth = selected_frame[GROUND_TRUTH_COLUMN]
+ moving_avg = ground_truth.rolling(window=WINDOW_SIZE).mean()
+
+ ax.scatter(selected_frame.index, ground_truth.to_numpy(), label="Measurement", color="salmon", marker="x", s=12, rasterized=True)
+ ax.plot(selected_frame.index, moving_avg, label="Moving Mean", color="red", linewidth=2)
+ ax.scatter(selected_frame.index, pretrained_prediction, label="Pretrained Model Prediction", color="dodgerblue", marker=".", s=15, rasterized=True)
+
+ colors = ["blueviolet", "lightblue", "lightgreen"]
+ for (checkpoint_path, label, _), (range_start_text, range_end_text) in zip(UPDATED_CHECKPOINTS, UPDATED_PLOT_RANGES, strict=True):
+ load_checkpoint(model, checkpoint_path)
+ updated_prediction = predict_in_batches(model, scaled_inputs, output_scaler)
+ range_start = align_timestamp(range_start_text, selected_frame.index)
+ mask = selected_frame.index >= range_start
+ if range_end_text is not None:
+ range_end = align_timestamp(range_end_text, selected_frame.index)
+ mask &= selected_frame.index < range_end
+ ax.scatter(selected_frame.index[mask], updated_prediction[mask], label=label, color=colors.pop(0), marker=".", s=15, rasterized=True)
+ del updated_prediction
+ gc.collect()
+
+ ax.set_xlabel("Time", fontsize=fontsize)
+ ax.set_ylabel("HXR pulse intensity (mJ)", fontsize=fontsize)
+ # ax.set_xlim(
+ # align_timestamp("2026-03-13 00:00", selected_frame.index),
+ # align_timestamp("2026-04-01 06:00", selected_frame.index),
+ # )
+ # ax.set_ylim([0, 6])
+ ax.xaxis.set_major_locator(mdates.AutoDateLocator())
+ ax.xaxis.set_major_formatter(date_format)
+
+ for drift_index, (start_text, end_text) in enumerate(DRIFTS, start=1):
+ ax.axvspan(
+ align_timestamp(start_text, selected_frame.index),
+ align_timestamp(end_text, selected_frame.index),
+ color="green",
+ alpha=0.3,
+ #label=f"Drift {drift_index}",
+ )
+
+
+
+ ax.legend(fontsize=12, loc="upper left")
+ ax.tick_params(labelsize=fontsize)
+ ax.tick_params(axis="x", rotation=45)
+ ax.grid(True, which="both", linestyle="--", linewidth=0.5)
+ fig.tight_layout()
+ fig.savefig(OUTPUT_PATH, dpi=200)
+ plt.close(fig)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/examples/slac_fel/plot_data.ipynb b/examples/slac_fel/plot_data.ipynb
new file mode 100644
index 0000000..68b9571
--- /dev/null
+++ b/examples/slac_fel/plot_data.ipynb
@@ -0,0 +1,352 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "097db665",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import sys\n",
+ "\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import torch\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib.dates as mdates\n",
+ "\n",
+ "# Make the apeiron package and the example modules importable from the repo root.\n",
+ "REPO_ROOT = os.path.abspath(\"../..\")\n",
+ "if REPO_ROOT not in sys.path:\n",
+ " sys.path.insert(0, REPO_ROOT)\n",
+ "\n",
+ "from examples.slac_fel.model import FELNet\n",
+ "from examples.slac_fel.utils import (\n",
+ " discover_window_files,\n",
+ " load_feature_config,\n",
+ " load_scalers,\n",
+ " load_window_file,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "21045a91",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "341 input features, target = ['GDET:FEE1:241:ENRC']\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/smiskov/miniconda3/envs/base-sim/lib/python3.13/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+ " from .autonotebook import tqdm as notebook_tqdm\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Paths\n",
+ "CONFIG_DIR = \"model\" # scalers + feature_config.yml\n",
+ "DATA_DIR = \"data\" # all data_*.pkl windows\n",
+ "ORIGINAL_MODEL_PATH = \"model/final_lcls_fel_model.pt\" # baseline model\n",
+ "ADAPTED_CKPT_PATH = \"../../output/slac-fel/drift_adaptation_3.pt\" # last CL checkpoint\n",
+ "\n",
+ "DEVICE = \"cpu\"\n",
+ "\n",
+ "input_cols, output_cols = load_feature_config(CONFIG_DIR)\n",
+ "input_scaler, output_scaler = load_scalers(CONFIG_DIR, device=DEVICE)\n",
+ "print(f\"{len(input_cols)} input features, target = {output_cols}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "8363e917",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Loaded original model and adapted checkpoint: drift_adaptation_3.pt\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Load both models.\n",
+ "# Original baseline is a raw nn.Sequential saved directly.\n",
+ "original_model = torch.load(ORIGINAL_MODEL_PATH, weights_only=False, map_location=DEVICE)\n",
+ "original_model.eval()\n",
+ "\n",
+ "# The drift-adaptation checkpoint is a FELNet state_dict.\n",
+ "adapted_model = FELNet(input_size=len(input_cols), output_size=len(output_cols))\n",
+ "adapted_state = torch.load(ADAPTED_CKPT_PATH, weights_only=False, map_location=DEVICE)\n",
+ "adapted_model.load_state_dict(adapted_state, strict=True)\n",
+ "adapted_model.eval()\n",
+ "print(\"Loaded original model and adapted checkpoint:\", os.path.basename(ADAPTED_CKPT_PATH))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "727cd6c0",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Found 308 window files\n",
+ " processed 50/308 files\n",
+ " processed 100/308 files\n",
+ " processed 150/308 files\n",
+ " processed 200/308 files\n",
+ " processed 250/308 files\n",
+ " processed 300/308 files\n",
+ " processed 308/308 files\n",
+ "Combined timeseries: 1,535,405 samples from 2026-03-04 00:14:32.394081280-08:00 to 2026-03-31 23:59:59.707322368-07:00\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
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